Use of Data Science in Banking for Fraud Detection

Data Science is a field that needs programming skills, and statistical and mathematical skills. This is needed to extract the insights of data. Technological advancement helps users to manage data easily in different fields. The field of data science is getting vast these days. The data managed by fields each day is very huge. There is a need for coding to extract these data insights easily and efficiently. Even if the business is big or small, the data they have to manage is always huge. So regardless of the size of the business or field, data science is an essential factor for them.

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Banking is one industry that depends heavily on this data science. The AI tools, data management, statistical tools, and knowledge in mathematics tools all are a great help to the banking industry. It helps the industry to attract customers towards the industry. It helps in surviving fierce competition from the rivals. It also helps in increasing the loyalty of the existing customers. One important help of data science in banking is the help in making effective data-driven decisions. Most of the data science job demands are now coming from the banking industry.

Role of Data Science in Banking

Data Science is an essential factor in the banking sector at all levels. It has many implications in the banking sector. It helps in managing huge data, surviving competition, and helps in data-driven decision-making. Let us look at some of the key roles that data science plays in the Banking sector.

1. Risk Modelling

One important role which has high priority is the banking sector. The performance can be assessed using risk modeling. One of the important risk modeling aspects is credit risk modeling. This helps the banks to analyze how the loans can be repaid. Risk modeling helps to formulate strategies to reinforce the lending schemes.

2. Customer Value

Customer value or Customer lifetime value is another key role of data science in banking. Customer is an essential part of the banking sector. Customers have to be attracted to the banks. The customers have to be retained and they need to be valued. Banks need to satisfy their customers. Data science helps in nurturing the relationship between the customer and the bank.

3. Customer Segmentation

Customer Segmentation is the process of grouping the customers based on their behavior of the customer, common characteristics, etc. Classification of the customers is done o the basis of the common behavior of the customers. Strengthening the relationship with the customers is one important aim in the process of customer segmentation. This is settled with the help of data science.

4. Recommendations

Banks provide offers and services to their customers based on the transactions done by them. By doing this the customers can easily find out the priority options and it improves the healthy relationship between customers and the bank. Banks used two types of recommendation engines, one is user-based and the other is Item-based.

5. Real-time Prediction and Analysis

This is the process of using computational techniques to predict the future. Data Science and machine learning are ideal tools for prediction and analysis. This is done by almost all fields. But in the field of banking, it has great importance as financial fluctuations prediction will help in planning the future and survive any negative impacts. The two major analytics techniques are Real-time analytics and Predictive analytics.

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Data Science in Banking for Fraud Detection

In all businesses, there will be incorrect entries and frauds. Like this, it will also happen in the banking industry. With the advancement in data science and machine learning, it is easy to find out irregularities and frauds. It involves the analysis and monitoring of user activities. The fraud detection process includes:

• Test Dataset
• Preprocessing data
• K-Meand Clustering
• Feature selection based on KNN
• SVM Classifier
• Applying SVM on Test Data
• Fraud or Not Fraud

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Conclusion

Data Science has different branches and features which have great implications in different fields. Like this, it is also helpful in the banking industry. The irregularities or fraud detection can be found using the help of data science.

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